Presenting a new methodology in estimating TBM-EPB machine face pressure: A case study

With the increasing expansion of urban environments, the creation and development of intra-city transportation systems in order to reduce traffic, pollution and reduce the costs of intra-city traffic is essential. Considering that an important part of the construction cost of the metro is related to the excavation and maintenance of tunnels. Therefore, one of the most important decisions in the construction of subway tunnels is the excavation method in alluvial and fall environments. Tunnel excavation by TBM-EPB machine is a fast, powerful and maintenance method compared to other excavation methods in soft soils and fall areas. One of the most important factors in preventing the face pressure from falling during excavation in soft and alluvial fields is estimating the optimal face pressure of the excavation machine in each excavation stage (different kilometers). Because the high or low face pressure of the excavation machine leads to increased costs, loss of life, high hardness and also leads to delays in the completion of the project. In this paper, due to the uncertainty in geotechnical parameters and the sensitivity of urban tunnels, the issue has been studied from a probabilistic perspective. For this purpose, first for 50 different numerical modeling modes of Tabriz Metro Line 2 using PLAXIS3D2020 software and then Monte Carlo simulation method has been used to generate random numbers and assign appropriate probabilistic distributions. Then, using the Gray Wolf meta-heuristic algorithm (GWO), the face pressure of the TBM-EPB machine was estimated using the prediction relation. Finally, in order to evaluate and validate the relationship, the statistical indicators of square correlation coefficient (R2), variance inclusion (VAF), mean absolute error percentage (MAPE), root mean square error (RMSE) and mean square error (MSE) were used. Is. According to the model validation, the relationship created by the gray wolf algorithm is very close to the reality of the problem and it can be used to continue the route in other similar areas.